In addition to model-specific approaches, expressible functional space can be investigated in a cross-model manner.
Specifically, Khrulkov et al. [1]} study the expressive capacity of recurrent neural networks (RNNs). They investigate the connections between network architectures and tensor decompositions, then make comparison between the expressive capacity of RNNs, CNNs, and shallow FCNNs.
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